{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4dc1bf63",
   "metadata": {},
   "source": [
    "# 10 - Geo and Switchback Experiments\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a6d9bc21",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:36.002601Z",
     "start_time": "2023-05-17T10:32:34.274958Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "from toolz.curried import *\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib\n",
    "from cycler import cycler\n",
    "\n",
    "color=['0.0', '0.4', '0.8']\n",
    "default_cycler = (cycler(color=color))\n",
    "linestyle=['-', '--', ':', '-.']\n",
    "marker=['o', 'v', 'd', 'p']\n",
    "\n",
    "plt.rc('axes', prop_cycle=default_cycler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1d121089",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:36.201716Z",
     "start_time": "2023-05-17T10:32:36.004416Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, RegressorMixin\n",
    "from sklearn.utils.validation import check_X_y, check_array, check_is_fitted\n",
    "import cvxpy as cp\n",
    "\n",
    "class SyntheticControl(BaseEstimator, RegressorMixin):\n",
    "\n",
    "    def __init__(self, fit_intercept=False):\n",
    "        self.fit_intercept = fit_intercept\n",
    "\n",
    "    def fit(self, y_pre_co, y_pre_tr):\n",
    "\n",
    "        y_pre_co, y_pre_tr = check_X_y(y_pre_co, y_pre_tr)\n",
    "        \n",
    "        # add intercept\n",
    "        intercept = np.ones((y_pre_co.shape[0], 1))*self.fit_intercept\n",
    "        X = np.concatenate([intercept, y_pre_co], axis=1)\n",
    "        w = cp.Variable(X.shape[1])\n",
    "        \n",
    "        objective = cp.Minimize(cp.sum_squares(X@w - y_pre_tr))\n",
    "        constraints = [cp.sum(w[1:]) == 1, w[1:] >= 0]\n",
    "        \n",
    "        problem = cp.Problem(objective, constraints)\n",
    "        \n",
    "        self.loss_ = problem.solve(eps_abs=1)\n",
    "        self.w_ = w.value[1:] \n",
    "        self.intercept_ = w.value[0] \n",
    "        \n",
    "        self.is_fitted_ = True\n",
    "        return self\n",
    "        \n",
    "        \n",
    "    def predict(self, y_co):\n",
    "\n",
    "        check_is_fitted(self)\n",
    "        y_co = check_array(y_co)\n",
    "        \n",
    "        return y_co @ self.w_ + self.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6823c18",
   "metadata": {},
   "source": [
    "## Geo-Experiments\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "48236235",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:36.225593Z",
     "start_time": "2023-05-17T10:32:36.202716Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>app_download</th>\n",
       "      <th>population</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>date</th>\n",
       "      <th>post</th>\n",
       "      <th>treated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3066.0</td>\n",
       "      <td>12396372</td>\n",
       "      <td>sao_paulo</td>\n",
       "      <td>sao_paulo</td>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2701.0</td>\n",
       "      <td>12396372</td>\n",
       "      <td>sao_paulo</td>\n",
       "      <td>sao_paulo</td>\n",
       "      <td>2022-03-02</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1927.0</td>\n",
       "      <td>12396372</td>\n",
       "      <td>sao_paulo</td>\n",
       "      <td>sao_paulo</td>\n",
       "      <td>2022-03-03</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1451.0</td>\n",
       "      <td>12396372</td>\n",
       "      <td>sao_paulo</td>\n",
       "      <td>sao_paulo</td>\n",
       "      <td>2022-03-04</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1248.0</td>\n",
       "      <td>12396372</td>\n",
       "      <td>sao_paulo</td>\n",
       "      <td>sao_paulo</td>\n",
       "      <td>2022-03-05</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   app_download  population       city      state       date  post  treated\n",
       "0        3066.0    12396372  sao_paulo  sao_paulo 2022-03-01     0        1\n",
       "1        2701.0    12396372  sao_paulo  sao_paulo 2022-03-02     0        1\n",
       "2        1927.0    12396372  sao_paulo  sao_paulo 2022-03-03     0        1\n",
       "3        1451.0    12396372  sao_paulo  sao_paulo 2022-03-04     0        1\n",
       "4        1248.0    12396372  sao_paulo  sao_paulo 2022-03-05     0        1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = (pd.read_csv(\"./data/online_mkt.csv\")\n",
    "      .astype({\"date\":\"datetime64[ns]\"})\n",
    "      .query(\"post==0\"))\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "63544218",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:36.231859Z",
     "start_time": "2023-05-17T10:32:36.227486Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36663.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detectable_diff = df[\"app_download\"].mean()*0.05\n",
    "sigma_2 = df.groupby(\"city\")[\"app_download\"].mean().var()\n",
    "\n",
    "np.ceil((sigma_2*16)/(detectable_diff)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccd05419",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-01T13:59:24.858215Z",
     "start_time": "2023-02-01T13:59:24.842541Z"
    }
   },
   "source": [
    "\n",
    "## Synthetic Control Design\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "93126bbb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:36.234676Z",
     "start_time": "2023-05-17T10:32:36.232782Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1f955293",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:36.246475Z",
     "start_time": "2023-05-17T10:32:36.235796Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>city</th>\n",
       "      <th>ananindeua</th>\n",
       "      <th>aparecida_de_goiania</th>\n",
       "      <th>aracaju</th>\n",
       "      <th>...</th>\n",
       "      <th>teresina</th>\n",
       "      <th>uberlandia</th>\n",
       "      <th>vila_velha</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-03-01</th>\n",
       "      <td>11.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>...</td>\n",
       "      <td>68.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-02</th>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-03</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>55.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-04</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>49.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-05</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "city        ananindeua  aparecida_de_goiania  aracaju  ...  teresina  \\\n",
       "date                                                   ...             \n",
       "2022-03-01        11.0                  54.0     65.0  ...      68.0   \n",
       "2022-03-02         5.0                  20.0     42.0  ...      17.0   \n",
       "2022-03-03         2.0                   0.0      0.0  ...      55.0   \n",
       "2022-03-04         0.0                   0.0     11.0  ...      49.0   \n",
       "2022-03-05         5.0                   5.0      0.0  ...      31.0   \n",
       "\n",
       "city        uberlandia  vila_velha  \n",
       "date                                \n",
       "2022-03-01        29.0        63.0  \n",
       "2022-03-02        29.0        11.0  \n",
       "2022-03-03        30.0        14.0  \n",
       "2022-03-04        35.0         0.0  \n",
       "2022-03-05         6.0         1.0  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_piv = (df\n",
    "          .pivot(\"date\", \"city\", \"app_download\"))\n",
    "\n",
    "df_piv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ff3ad9fe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:36.252696Z",
     "start_time": "2023-05-17T10:32:36.248107Z"
    }
   },
   "outputs": [],
   "source": [
    "f = (df.groupby(\"city\")[\"population\"].first()\n",
    "     /df.groupby(\"city\")[\"population\"].first().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "46ca33e3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:36.613672Z",
     "start_time": "2023-05-17T10:32:36.253974Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,3))\n",
    "plt.bar(f.sort_values().index, f.sort_values().values)\n",
    "plt.title(\"Size of each city as a proportional of the market (f vector)\")\n",
    "plt.xticks(rotation=90, fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a53093b0",
   "metadata": {},
   "source": [
    "### Trying a Random Set of Treated Units\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dbb2a72f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:36.617198Z",
     "start_time": "2023-05-17T10:32:36.614845Z"
    }
   },
   "outputs": [],
   "source": [
    "y_avg = df_piv.dot(f)\n",
    "geos = list(df_piv.columns)\n",
    "n_tr = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "db952541",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:36.623753Z",
     "start_time": "2023-05-17T10:32:36.620427Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['manaus', 'recife', 'sao_bernardo_do_campo', 'salvador', 'aracaju'],\n",
       "      dtype='<U23')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "rand_geos = np.random.choice(geos, n_tr, replace=False)\n",
    "rand_geos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d8462027",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:36.655809Z",
     "start_time": "2023-05-17T10:32:36.624853Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'geos': array(['salvador', 'aracaju'], dtype='<U23'),\n",
       " 'loss': 1598616.80875266}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_sc(geos, df_sc, y_mean_pre):\n",
    "    \n",
    "    model = SyntheticControl(fit_intercept=True)\n",
    "    model.fit(df_sc[geos], y_mean_pre)\n",
    "    \n",
    "    selected_geos = geos[np.abs(model.w_) > 1e-5]\n",
    "    \n",
    "    return {\"geos\": selected_geos, \"loss\": model.loss_ }\n",
    "\n",
    "\n",
    "get_sc(rand_geos, df_piv, y_avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bbeab3ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:36.677060Z",
     "start_time": "2023-05-17T10:32:36.657134Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_sc_st_combination(treatment_geos, df_sc, y_mean_pre):\n",
    "    \n",
    "    treatment_result = get_sc(treatment_geos, df_sc, y_mean_pre)\n",
    "    \n",
    "    remaining_geos = df_sc.drop(\n",
    "        columns=treatment_result[\"geos\"]\n",
    "    ).columns\n",
    "    \n",
    "    control_result = get_sc(remaining_geos, df_sc, y_mean_pre)\n",
    "\n",
    "    return {\"st_geos\": treatment_result[\"geos\"],\n",
    "            \"sc_geos\": control_result[\"geos\"],\n",
    "            \"loss\": treatment_result[\"loss\"] + control_result[\"loss\"]}\n",
    "\n",
    "\n",
    "resulting_geos = get_sc_st_combination(rand_geos, df_piv, y_avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6501bf3f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:36.681609Z",
     "start_time": "2023-05-17T10:32:36.678373Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['salvador', 'aracaju'], dtype='<U23')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resulting_geos.get(\"st_geos\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "28b292c9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:36.685656Z",
     "start_time": "2023-05-17T10:32:36.683035Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(resulting_geos.get(\"st_geos\")) + len(resulting_geos.get(\"sc_geos\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "94c0d6ac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:36.803578Z",
     "start_time": "2023-05-17T10:32:36.686898Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc39cfb2ad0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "synthetic_tr = SyntheticControl(fit_intercept=True)\n",
    "synthetic_co = SyntheticControl(fit_intercept=True)\n",
    "\n",
    "synthetic_tr.fit(df_piv[resulting_geos.get(\"st_geos\")], y_avg)\n",
    "synthetic_co.fit(df_piv[resulting_geos.get(\"sc_geos\")], y_avg)\n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.plot(y_avg, label=\"Average\")\n",
    "plt.plot(y_avg.index, synthetic_co.predict(df_piv[resulting_geos.get(\"sc_geos\")]), label=\"sc\", ls=\":\")\n",
    "plt.plot(y_avg.index, synthetic_tr.predict(df_piv[resulting_geos.get(\"st_geos\")]), label=\"st\")\n",
    "\n",
    "\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcf9d5e6",
   "metadata": {},
   "source": [
    "### Random Search\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "88d54a7b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:43.389975Z",
     "start_time": "2023-05-17T10:32:36.805186Z"
    }
   },
   "outputs": [],
   "source": [
    "from joblib import Parallel, delayed\n",
    "from toolz import partial\n",
    "\n",
    "np.random.seed(1)\n",
    "geo_samples = [np.random.choice(geos, n_tr, replace=False) \n",
    "               for _ in range(1000)]\n",
    "\n",
    "est_combination = partial(get_sc_st_combination,\n",
    "                          df_sc=df_piv,\n",
    "                          y_mean_pre=y_avg)\n",
    "\n",
    "results = Parallel(n_jobs=4)(delayed(est_combination)(geos)\n",
    "                             for geos in geo_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "007f44cf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:43.396893Z",
     "start_time": "2023-05-17T10:32:43.392013Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['nova_iguacu', 'belem', 'joinville', 'sao_paulo'], dtype='<U23')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resulting_geos = min(results, key=lambda x: x.get(\"loss\"))\n",
    "resulting_geos.get(\"st_geos\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c7d52f86",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:43.630748Z",
     "start_time": "2023-05-17T10:32:43.398336Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc39c6c0150>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "synthetic_tr = SyntheticControl(fit_intercept=True)\n",
    "synthetic_co = SyntheticControl(fit_intercept=True)\n",
    "\n",
    "synthetic_tr.fit(df_piv[resulting_geos.get(\"st_geos\")], y_avg)\n",
    "synthetic_co.fit(df_piv[resulting_geos.get(\"sc_geos\")], y_avg)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.plot(y_avg, label=\"Average\")\n",
    "plt.plot(y_avg.index, synthetic_co.predict(df_piv[resulting_geos.get(\"sc_geos\")]), label=\"sc\", ls=\":\")\n",
    "plt.plot(y_avg.index, synthetic_tr.predict(df_piv[resulting_geos.get(\"st_geos\")]), label=\"st\")\n",
    "\n",
    "\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be13b10c",
   "metadata": {},
   "source": [
    "## Switchback Experiment\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a2b25f39",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:43.641109Z",
     "start_time": "2023-05-17T10:32:43.632399Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>d</th>\n",
       "      <th>delivery_time</th>\n",
       "      <th>delivery_time_1</th>\n",
       "      <th>delivery_time_0</th>\n",
       "      <th>tau</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2.84</td>\n",
       "      <td>2.84</td>\n",
       "      <td>5.84</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>4.49</td>\n",
       "      <td>1.49</td>\n",
       "      <td>6.49</td>\n",
       "      <td>-5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7.27</td>\n",
       "      <td>2.27</td>\n",
       "      <td>8.27</td>\n",
       "      <td>-6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>5.27</td>\n",
       "      <td>2.27</td>\n",
       "      <td>8.27</td>\n",
       "      <td>-6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5.59</td>\n",
       "      <td>4.59</td>\n",
       "      <td>10.59</td>\n",
       "      <td>-6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   d  delivery_time  delivery_time_1  delivery_time_0  tau\n",
       "0  1           2.84             2.84             5.84 -3.0\n",
       "1  0           4.49             1.49             6.49 -5.0\n",
       "2  0           7.27             2.27             8.27 -6.0\n",
       "3  1           5.27             2.27             8.27 -6.0\n",
       "4  1           5.59             4.59            10.59 -6.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./data/sb_exp_every.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ea4f5a64",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:43.755523Z",
     "start_time": "2023-05-17T10:32:43.642428Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc39d024450>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "x = df.index\n",
    "plt.plot(x, df[\"delivery_time_0\"], ls=\":\", color=\"0.0\", label=\"Y0\")\n",
    "plt.plot(x, df[\"delivery_time_1\"], ls=\":\", color=\"0.8\", label=\"Y1\")\n",
    "plt.plot(x, df[\"delivery_time\"], color=\"0.4\", label=\"Y\")\n",
    "plt.scatter(x[df[\"d\"]==1], df[\"delivery_time\"][df[\"d\"]==1], label=\"Treated\", marker=\"v\", color=\"0.4\")\n",
    "plt.scatter(x[df[\"d\"]==0], df[\"delivery_time\"][df[\"d\"]==0], label=\"Control\", marker=\"o\", color=\"0.4\")\n",
    "plt.ylabel(\"delivery_time\")\n",
    "plt.xlabel(\"Hours\")\n",
    "plt.legend(fontsize=14)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c78e579f",
   "metadata": {},
   "source": [
    "### Potential Outcomes of Sequences"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ffaa6db",
   "metadata": {},
   "source": [
    "### Estimating the Order of Carryover Effect\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4e415d48",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:43.767150Z",
     "start_time": "2023-05-17T10:32:43.756614Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>d_l0</th>\n",
       "      <th>d_l1</th>\n",
       "      <th>d_l2</th>\n",
       "      <th>...</th>\n",
       "      <th>d_l4</th>\n",
       "      <th>d_l5</th>\n",
       "      <th>d_l6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   d_l0  d_l1  d_l2  ...  d_l4  d_l5  d_l6\n",
       "0     1   NaN   NaN  ...   NaN   NaN   NaN\n",
       "1     0   1.0   NaN  ...   NaN   NaN   NaN\n",
       "2     0   0.0   1.0  ...   NaN   NaN   NaN\n",
       "3     1   0.0   0.0  ...   NaN   NaN   NaN\n",
       "4     1   1.0   0.0  ...   1.0   NaN   NaN\n",
       "\n",
       "[5 rows x 7 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lags = df.assign(**{\n",
    "    f\"d_l{l}\" : df[\"d\"].shift(l) for l in range(7)\n",
    "})\n",
    "\n",
    "df_lags[[f\"d_l{l}\" for l in range(7)]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7744a89d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:43.784211Z",
     "start_time": "2023-05-17T10:32:43.768761Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    9.3270</td> <td>    0.461</td> <td>   20.246</td> <td> 0.000</td> <td>    8.414</td> <td>   10.240</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d_l0</th>      <td>   -2.9645</td> <td>    0.335</td> <td>   -8.843</td> <td> 0.000</td> <td>   -3.629</td> <td>   -2.300</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d_l1</th>      <td>   -1.8861</td> <td>    0.339</td> <td>   -5.560</td> <td> 0.000</td> <td>   -2.559</td> <td>   -1.213</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d_l2</th>      <td>   -1.0013</td> <td>    0.340</td> <td>   -2.943</td> <td> 0.004</td> <td>   -1.676</td> <td>   -0.327</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d_l3</th>      <td>    0.2594</td> <td>    0.341</td> <td>    0.762</td> <td> 0.448</td> <td>   -0.416</td> <td>    0.935</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d_l4</th>      <td>    0.1431</td> <td>    0.340</td> <td>    0.421</td> <td> 0.675</td> <td>   -0.531</td> <td>    0.817</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d_l5</th>      <td>    0.1388</td> <td>    0.340</td> <td>    0.408</td> <td> 0.684</td> <td>   -0.536</td> <td>    0.813</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d_l6</th>      <td>    0.5588</td> <td>    0.336</td> <td>    1.662</td> <td> 0.099</td> <td>   -0.108</td> <td>    1.225</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "\n",
    "model = smf.ols(\"delivery_time ~\" + \"+\".join([f\"d_l{l}\"\n",
    "                                              for l in range(7)]),\n",
    "                data=df_lags).fit()\n",
    "\n",
    "model.summary().tables[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f7670aab",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:43.789188Z",
     "start_time": "2023-05-17T10:32:43.785459Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tau_m: -4.751686115272022\n",
      "95% CI: [-6.5087183781545574, -2.9946538523894857]\n"
     ]
    }
   ],
   "source": [
    "## remember to remove the intercept\n",
    "tau_m_hat = model.params[1:].sum() \n",
    "se_tau_m_hat = np.sqrt((model.bse[1:]**2).sum()) \n",
    "print(\"tau_m:\", tau_m_hat)\n",
    "print(\"95% CI:\", [tau_m_hat -1.96*se_tau_m_hat,\n",
    "                  tau_m_hat +1.96*se_tau_m_hat])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "490a7f7b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:43.793581Z",
     "start_time": "2023-05-17T10:32:43.790408Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tau_m: -5.8518568954422925\n",
      "95% CI: [-7.000105171362163, -4.703608619522422]\n"
     ]
    }
   ],
   "source": [
    "## selecting lags 0, 1 and 2\n",
    "tau_m_hat = model.params[1:4].sum() \n",
    "se_tau_m_hat = np.sqrt((model.bse[1:4]**2).sum()) \n",
    "print(\"tau_m:\", tau_m_hat)\n",
    "print(\"95% CI:\", [tau_m_hat -1.96*se_tau_m_hat,\n",
    "                  tau_m_hat +1.96*se_tau_m_hat])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85b64be6",
   "metadata": {},
   "source": [
    "### Design Based Estimation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "17ea55a8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:43.797972Z",
     "start_time": "2023-05-17T10:32:43.794785Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False, False,  True, False, False])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rad_points_3 = np.array([True, False, False]*(2))\n",
    "rad_points_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c4e1a227",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:43.802452Z",
     "start_time": "2023-05-17T10:32:43.799531Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 2, 2, 2])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rad_points_3.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a0e87646",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:43.807183Z",
     "start_time": "2023-05-17T10:32:43.803781Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1],\n",
       "       [1, 1, 2],\n",
       "       [1, 2, 2],\n",
       "       [2, 2, 2]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numpy.lib.stride_tricks import sliding_window_view\n",
    "\n",
    "m = 2\n",
    "sliding_window_view(rad_points_3.cumsum(), window_shape=m+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ec17f6a8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:43.817003Z",
     "start_time": "2023-05-17T10:32:43.814228Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0],\n",
       "       [0, 1],\n",
       "       [1, 0],\n",
       "       [0, 0]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diff(sliding_window_view(rad_points_3.cumsum(), 3), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "207c8532",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:43.821854Z",
     "start_time": "2023-05-17T10:32:43.818315Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, nan,  1.,  2.,  2.,  1.])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_rand_windows = np.concatenate([\n",
    "    [np.nan]*m,\n",
    "    np.diff(sliding_window_view(rad_points_3.cumsum(), 3),\n",
    "            axis=1).sum(axis=1)+1\n",
    "])\n",
    "\n",
    "n_rand_windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "af4ba9c1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:43.826011Z",
     "start_time": "2023-05-17T10:32:43.823303Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ nan,  nan, 0.5 , 0.25, 0.25, 0.5 ])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p=0.5\n",
    "p**n_rand_windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bbab7d2f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:43.831898Z",
     "start_time": "2023-05-17T10:32:43.827411Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  nan,   nan, 0.125, 0.125, 0.125, 0.125])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def compute_p(rand_points, m, p=0.5):\n",
    "    n_windows_last_m = np.concatenate([\n",
    "        [np.nan]*m,\n",
    "        np.diff(sliding_window_view(rand_points.cumsum(), m+1),\n",
    "                axis=1).sum(axis=1)+1\n",
    "    ])\n",
    "    return p**n_windows_last_m\n",
    "\n",
    "compute_p(np.ones(6)==1, 2, 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ebae0381",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:43.836625Z",
     "start_time": "2023-05-17T10:32:43.833381Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ nan,  nan, 0.5 , 0.25, 0.25, 0.25, 0.25])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand_points = np.array([True, False, False, True, False, True, False])\n",
    "compute_p(rand_points, 2, 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a6951c64",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:43.841438Z",
     "start_time": "2023-05-17T10:32:43.838103Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nan nan  1.  0.  0.  0.]\n",
      "[nan nan  0.  0.  0.  1.]\n"
     ]
    }
   ],
   "source": [
    "def last_m_d_equal(d_vec, d, m):\n",
    "    return np.concatenate([\n",
    "        [np.nan]*m,\n",
    "        (sliding_window_view(d_vec, m+1)==d).all(axis=1)\n",
    "    ])\n",
    "\n",
    "print(last_m_d_equal([1, 1, 1, 0, 0, 0], 1, m=2))\n",
    "print(last_m_d_equal([1, 1, 1, 0, 0, 0], 0, m=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9aa61e6f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:43.845747Z",
     "start_time": "2023-05-17T10:32:43.842807Z"
    }
   },
   "outputs": [],
   "source": [
    "def ipw_switchback(d, y, rand_points, m, p=0.5):\n",
    "    \n",
    "    p_last_m_equal_1 = compute_p(rand_points, m=m, p=p)\n",
    "    p_last_m_equal_0 = compute_p(rand_points, m=m, p=1-p)\n",
    "    \n",
    "    last_m_is_1 = last_m_d_equal(d,1,m)\n",
    "    last_m_is_0 = last_m_d_equal(d,0,m)\n",
    "  \n",
    "    y1_rec = y*last_m_is_1/p_last_m_equal_1\n",
    "    y0_rec = y*last_m_is_0/p_last_m_equal_0\n",
    "    \n",
    "    return np.mean((y1_rec-y0_rec)[m:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b43bb38f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:43.850941Z",
     "start_time": "2023-05-17T10:32:43.847008Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-7.426440677966101"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipw_switchback(df[\"d\"],\n",
    "               df[\"delivery_time\"],\n",
    "               np.ones(len(df))==1,\n",
    "               m=2, p=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8161b47c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:46.415270Z",
     "start_time": "2023-05-17T10:32:43.852488Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc3503a9790>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from statsmodels.tsa.arima_process import ArmaProcess\n",
    "\n",
    "\n",
    "def gen_d(rand_points, p):\n",
    "    result = [np.random.binomial(1, p)]\n",
    "    \n",
    "    for t in rand_points[1:]:\n",
    "        result.append(np.random.binomial(1, p)*t + (1-t)*result[-1])\n",
    "    \n",
    "    return np.array(result)\n",
    "\n",
    "\n",
    "def y_given_d(d, effect_params, T, seed=None):\n",
    "    np.random.seed(seed) if not seed is None else None\n",
    "    x = np.arange(1, T+1)\n",
    "    return (np.log(x+1)\n",
    "            + 2*np.sin(x*2*np.pi/24)\n",
    "            + np.convolve(~d.astype(bool), effect_params)[:-(len(effect_params)-1)]\n",
    "            + ArmaProcess([3,2,1], 12).generate_sample(T)).round(2)\n",
    "\n",
    "\n",
    "\n",
    "def gen_data_rand_every():\n",
    "    effect_params = [3,2,1]\n",
    "    T=120\n",
    "    p=0.5\n",
    "    m=2\n",
    "    \n",
    "    d = np.random.binomial(1, 0.5, T)\n",
    "    y = y_given_d(d, [3,2,1], T)\n",
    "    rand_points = np.ones(T)==1\n",
    "    \n",
    "    return pd.DataFrame(dict(d=d, y=y, rand_points=rand_points))\n",
    "\n",
    "\n",
    "def tau_ols(df):\n",
    "    df_lags = df.assign(**{\n",
    "        f\"d_l{l}\" : df[\"d\"].shift(l) for l in range(3)\n",
    "    })\n",
    "    model = smf.ols(\"y ~\" + \"+\".join([f\"d_l{l}\" for l in range(3)]),data=df_lags).fit()\n",
    "    return model.params[1:].sum()\n",
    "\n",
    "def tau_ipw(df):\n",
    "    return ipw_switchback(df[\"d\"],\n",
    "                          df[\"y\"],\n",
    "                          df[\"rand_points\"],\n",
    "                          m=2, p=0.5)\n",
    "    \n",
    "\n",
    "    \n",
    "np.random.seed(123)\n",
    "exps_dfs = [gen_data_rand_every() for _ in range(500)]\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(8, 4))\n",
    "ols_taus = list(map(tau_ols, exps_dfs))\n",
    "ax1.hist(ols_taus, label=\"OLS tau\", color=\"0.5\")\n",
    "ax1.vlines(-6, 0, 120, color=\"0\", label=\"True tau\")\n",
    "ax1.legend()\n",
    "\n",
    "ipw_taus = list(map(tau_ipw, exps_dfs))\n",
    "ax2.hist(ipw_taus, label=\"IPW tau\", color=\"0.5\");\n",
    "ax2.vlines(-6, 0, 120,  color=\"0\")\n",
    "ax2.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ab31b80",
   "metadata": {},
   "source": [
    "### Optimal Switchback Design\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e6e20cc3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:46.421046Z",
     "start_time": "2023-05-17T10:32:46.417019Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = 2\n",
    "T = 12\n",
    "n = T/m\n",
    "np.isin(\n",
    "    np.arange(1, T+1),\n",
    "    [1] + [i*m+1 for i in range(2, int(n)-1)]\n",
    ")*1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ef05c2bc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:46.426064Z",
     "start_time": "2023-05-17T10:32:46.422419Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = 3\n",
    "T = 15\n",
    "n = T/m\n",
    "np.isin(\n",
    "    np.arange(1, T+1),\n",
    "    [1] + [i*m+1 for i in range(2, int(n)-1)]\n",
    ")*1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "165d9236",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:48.844088Z",
     "start_time": "2023-05-17T10:32:46.427594Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "def gen_d(rand_points, p):\n",
    "    result = [np.random.binomial(1, p)]\n",
    "    \n",
    "    for t in rand_points[1:]:\n",
    "        result.append(np.random.binomial(1, p)*t + (1-t)*result[-1])\n",
    "    \n",
    "    return np.array(result)\n",
    "\n",
    "T = 120\n",
    "m = 2\n",
    "\n",
    "def gen_exp(rand_points, T):\n",
    "    effect_params = [3,2,1]\n",
    "    p=0.5\n",
    "    \n",
    "    d = gen_d(rand_points, p=p)\n",
    "    y = y_given_d(d, [3,2,1], T)\n",
    "\n",
    "    return pd.DataFrame(dict(d=d, y=y, rand_points=rand_points))\n",
    "\n",
    "\n",
    "every_1 = np.array([True]*T)\n",
    "every_3 = np.array([True, False, False]*(T//3))\n",
    "n = T//m\n",
    "opt = np.isin(\n",
    "    np.arange(1, T+1),\n",
    "    [1] + [i*m+1 for i in range(2, int(n)-1)]\n",
    ")\n",
    "\n",
    "np.random.seed(123)\n",
    "exps_every_1 = [gen_exp(every_1, T) for _ in range(1000)]\n",
    "exps_every_3 = [gen_exp(every_3, T) for _ in range(1000)]\n",
    "exps_opt = [gen_exp(opt, T) for _ in range(1000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c1ef305e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:50.893740Z",
     "start_time": "2023-05-17T10:32:48.845325Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NBJZ0dILMvDwzGzOzsX///t0sQ5IkSZJqR3cD1mxgYun9RGBWu/YJEbFFRAwGhgAP9qxESZIkSaoP5SzTfh0wGugXEa1AEzANuCEiPgc8A5wAkJnzI+IGYAGwEpicmasqVLskSZIk1ZROA1ZmnrSOXYeto/+FwIU9KUqSJEmS6lHRi1xIkiRJ0ibLgCVJkiRJBen0FkFJkiS1aW5urnYJdaGpqanaJUhV4wyWJEmSJBXEgCVJkiRJBTFgSZIkSVJBDFiSJEmSVBADliRJkiQVxIAlSZIkSQUxYEmSJElSQQxYkiRJklQQA5YkSZIkFcSAJUmSJEkFMWBJkiRJUkEMWJIkSZJUEAOWJEmSJBVk82oXIG0qmpubq12CJEmSKswZLEmSJEkqiAFLkiRJkgpiwJIkSZKkghiwJEmSJKkgBixJkiRJKogBS5IkSZIKUrFl2iNiLPD/gF7Av2fmtEpdS9Xl8uOSJElSm4oErIjoBVwCHA60Ag9FxOzMXFCJ60mSJKl2+MvX8jQ1NVW7BFVApW4R3A9YlJl/yMy/ANcD4yp0LUmSJEmqCZW6RXAX4Nl2263A/u07RMQkYFJpc0VEPNnBefoBL1SkQlWaY1e/HLv65LjVL8euPjlu9atmxm7q1KnVLqGe1My4tbNbR42VCljRQVu+ayPzcuDy9Z4koiUzG4ssTBuGY1e/HLv65LjVL8euPjlu9cuxq0/1NG6VukWwFdi13fZAYEmFriVJkiRJNaFSAeshYEhEDI6I9wETgNkVupYkSZIk1YSK3CKYmSsj4kzgVtqWab8yM+d341TrvYVQNc2xq1+OXX1y3OqXY1efHLf65djVp7oZt8jMzntJkiRJkjpVqVsEJUmSJGmTY8CSJEmSpIIYsCRJkiSpIAYsSZIkSSqIAUuSJEmSCmLAkiRJkqSCGLAkSZIkqSAGLEmSJEkqiAFLkiRJkgpiwJIkSZKkghiwJEmSJKkgBixJkiRJKogBS5IkSZIKYsCSJFVMRNwaEed10D4uIv4UEZtX6LrnR8RjEbEyIqaup99VEZER8bfr6fN3EfFgRLwaEY9GxEHdPZckaeNnwJIkVdJ04NSIiLXaTwWuycyV5ZykG0FsEfBPwM3rOedBwAc7ue4OwGzgO8D7gW8Dv4iI7bt6LknSpsGAJUmqpJuAHYCPrm4ohZOjgNkRcX9EvBQRSyPi4oh4X7t+GRGTI2IhsLArF83MGZl5C/BqR/tLge1fgTM7OdXfAc9l5n9k5qrM/AmwDDiuG+eSJG0CDFiSpIrJzDeAG4DPtGs+EXgCWAGcBfQDDgQOA76w1imOBfYHhgKUbtF7aR2vS7tQ2lnAPZn5aCf9ovRau22vbpxLkrQJqMi975IktTMDuDkivlgKXJ8BZmTmnHZ9FkfEvwGHAD9o1/6tzHxx9UZm7t3TYiJiV+AMYEQZ3f8b2DkiTgJ+CpxM262AW3XjXJKkTYAzWJKkisrMe2m7rW5cRPwNMBK4NiJ2j4j/LC128QrwTdpms9p7tgIl/QA4LzNf7qxjZi4HxgH/ADwHjAXuAFq7ei5J0qbBgCVJ2hCupm3m6lTgtsx8DvgRbbcKDsnMbYGv897b8bL9RkTMj4gV63hdVmYthwHfKQW7P5Xa7o+IkzvqnJm/ycyRmblDqf4PAQ9251ySpI2ftwhKkjaEq4F/Bvam7ZklgD7AK8CKiPgw8HnaZrrWKTP3LOdiEdEb6EXbLxI3j4gG4O3MXAXszrt/wbgUOBr43TrOtQ8wD9gSOA9ozcxbS7u7dC5J0sbPGSxJUsVl5mLanmfamrZlzwG+StszTa8CVwAzC7zkFcAbwEnAuaX3p5ZqeT4z/7T6Ver/Qun5MCLisrVmw/4JeIG22xUHAJ9s97nWey5J0qYnMrPzXpIkSZKkTjmDJUmSJEkFMWBJkiRJUkEMWJIkSZJUEAOWJEmSJBWkJpZp79evXw4aNKjaZUiSJElSWebMmfNCZvZfu70mAtagQYNoaWmpdhmSJEmSVJaI+GNH7d4iKEmSJEkFMWBJkiRJUkEMWJIkSZJUkJp4BkuSpA2hubm52iUUoqmpqdolSCrT22+/TWtrK2+++Wa1S1E3NTQ0MHDgQHr37l1WfwOWJEmSVCGtra306dOHQYMGERHVLkddlJksX76c1tZWBg8eXNYx3iIoSZIkVcibb75J3759DVd1KiLo27dvl2YgDViSJElSBRmu6ltXx8+AJUmSJEkF8RksSZIkaQMperGdcha96dWrF8OGDVuzPWHCBKZMmVJoHevyf//v/2XWrFlsttlm7LjjjkyfPp2dd975XX0WL17MHnvswYc+9CH+8pe/cPDBB3PppZey2WblzQW1tLRw9dVX88Mf/rDsugYNGkRLSwv9+vXr0ucphwFLkiRJ2ohtueWWzJ07t9Bzrlq1il69enXa72tf+xrnn38+AD/84Q8577zzuOyyy97T74Mf/CBz585l5cqVHHroodx0000cd9xxnZ5/5cqVNDY20tjY2PUPUSHeIihJkiRtYm655RZOPPHENdt33303Rx99NAC33XYbBx54IPvuuy8nnHACK1asANpmfc477zwOOuggpk2bxr777rvm+IULFzJixIj3XGfbbbdd8/61117r9HmmzTffnL/7u79j0aJFLFu2jOOPP56RI0cycuRI7rvvPgCmTp3KpEmTGDNmDJ/5zGe4++67OeqoowB48cUXOfbYY9l777054IADePTRRwFYvnw5Y8aMYZ999uGMM84gM9fUdOSRR/KRj3yEvfbai5kzZ3b533JtBixJkiRpI/bGG28wfPjwNa+ZM2dy+OGH88ADD/Daa68BMHPmTMaPH88LL7zABRdcwB133MHDDz9MY2MjF1100ZpzNTQ0cO+993Luueey3XbbrZkZu+qqqzjttNM6vP65557LrrvuyjXXXMN555233lpff/117rzzToYNG8aXv/xlzjrrLB566CF+9rOfcfrpp6/pN2fOHGbNmsW11177ruObmprYZ599ePTRR/nmN7/JZz7zGaDt1syDDjqIRx55hGOOOYZnnnkGgF/96lfsvPPO/O53v2PevHmMHTu2S/+2HTFgSZIkSRux1bcIrn6NHz+ezTffnLFjx/KLX/yClStXcvPNNzNu3DgeeOABFixYwKhRoxg+fDgzZszgj3/845pzjR8/fs37008/nauuuopVq1Yxc+ZMTj755A6vf+GFF/Lss89yyimncPHFF3fY56mnnmL48OGMGjWKI488ko9//OPccccdnHnmmQwfPpxjjjmGV155hVdffRWAY445hi233PI957n33ns59dRTATj00ENZvnw5L7/8Mvfccw+f/vSnATjyyCPZfvvtARg2bBh33HEHZ599Nv/1X//Fdttt141/4XfzGSxJkiRpEzR+/HguueQSdthhB0aOHEmfPn3ITA4//HCuu+66Do/Zeuut17w//vjjaW5u5tBDD2XEiBH07dt3vdc7+eSTOfLIIztc6GP1M1jtvfPOO9x///0dBqn2dbS3+ta/9lbfltjR7Ym77747c+bM4Ze//CXnnHMOY8aM4Rvf+MZ6P0dnOp3BiohdI+LXEfF4RMyPiC+X2neIiNsjYmHp5/btjjknIhZFxJMRcUSPKpQkSZJUuNGjR/Pwww9zxRVXrJmZOuCAA7jvvvtYtGgR0HbL3u9///sOj29oaOCII47g85//PJ/97Gc77LNw4cI172fPns2HP/zhsusbM2bMu2a8ylmo4+CDD+aaa64B2p4r69evH9tuu+272m+55Rb+/Oc/A7BkyRK22morPv3pT/PVr36Vhx9+uOz61qWcGayVwD9m5sMR0QeYExG3A6cBd2bmtIiYAkwBzo6IocAEYE9gZ+COiNg9M1f1uFpJ0gZX9JLCkrQpK2dZ9aKtfgZrtbFjxzJt2jR69erFUUcdxfTp05kxYwYA/fv3Z/r06Zx00km89dZbAFxwwQXsvvvuHZ77lFNO4ec//zljxozpcP+UKVN48skn2Wyzzdhtt906XEFwXX74wx8yefJk9t57b1auXMnBBx/c6fFTp07ls5/9LHvvvTdbbbXVms/V1NTESSedxL777sshhxzCBz7wAQAee+wxvva1r7HZZpvRu3dvfvSjH5Vd37pER9No6z0gYhZwcek1OjOXRsQA4O7M/FBEnAOQmd8q9b8VmJqZ96/rnI2NjdnS0tLdzyBJqiADVu2pxv+gSeqexx9/nD322KPaZVTMd7/7XV5++eU1S7FvrDoax4iYk5nvWR++S89gRcQgYB/gt8BOmbkUoBSydix12wV4oN1hraW2tc81CZgErEmQkiRJkurDJz/5SZ566inuuuuuapdSU8oOWBGxDfAz4CuZ+cp61rDvaMd7psky83LgcmibwSq3DkmSJEnVd+ONN1a7hJpU1jLtEdGbtnB1TWb+vNT8XOnWQEo/ny+1twK7tjt8ILCkmHIlSZKk+tLVR3JUW7o6fuWsIhjAj4HHM/OidrtmAxNL7ycCs9q1T4iILSJiMDAEeLBLVUmSJEkbgYaGBpYvX27IqlOZyfLly2loaCj7mHJuERwFnAo8FhFzS21fB6YBN0TE54BngBNKRcyPiBuABbStQDjZFQQlSZK0KRo4cCCtra0sW7as2qWomxoaGhg4cGDZ/bu8imAluIqgJNUuVxFUJbkioqR6ta5VBMt6BkuSJEmS1DkDliRJkiQVxIAlSZIkSQUxYEmSJElSQQxYkiRJklQQA5YkSZIkFcSAJUmSJEkFMWBJkiRJUkEMWJIkSZJUEAOWJEmSJBXEgCVJkiRJBTFgSZIkSVJBOg1YEXFlRDwfEfPatU2NiP+JiLml1yfa7TsnIhZFxJMRcUSlCpckSZKkWlPODNZ0YGwH7d/PzOGl1y8BImIoMAHYs3TMpRHRq6hiJUmSJKmWdRqwMvMe4MUyzzcOuD4z38rMp4FFwH49qE+SJEmS6kZPnsE6MyIeLd1CuH2pbRfg2XZ9Wktt7xERkyKiJSJali1b1oMyJEmSJKk2dDdg/Qj4IDAcWAp8r9QeHfTNjk6QmZdnZmNmNvbv37+bZUiSJElS7ehWwMrM5zJzVWa+A1zBX28DbAV2bdd1ILCkZyVKkiRJUn3oVsCKiAHtNj8JrF5hcDYwISK2iIjBwBDgwZ6VKEmSJEn1YfPOOkTEdcBooF9EtAJNwOiIGE7b7X+LgTMAMnN+RNwALABWApMzc1VFKpckSZKkGtNpwMrMkzpo/vF6+l8IXNiToiRJkiSpHvVkFUFJkiRJUjudzmBJkrquubm52iVIkqQqcAZLkiRJkgpiwJIkSZKkghiwJEmSJKkgBixJkiRJKogBS5IkSZIKYsCSJEmSpIIYsCRJkiSpIAYsSZIkSSqIAUuSJEmSCmLAkiRJkqSCdBqwIuLKiHg+Iua1a9shIm6PiIWln9u323dORCyKiCcj4ohKFS5JkiRJtaacGazpwNi12qYAd2bmEODO0jYRMRSYAOxZOubSiOhVWLWSJEmSVMM6DViZeQ/w4lrN44AZpfczgGPbtV+fmW9l5tPAImC/YkqVJEmSpNq2eTeP2ykzlwJk5tKI2LHUvgvwQLt+raW294iIScAkgA984APdLEOSJNWz5ubmapdQmKampmqXIKkGFL3IRXTQlh11zMzLM7MxMxv79+9fcBmSJEmStOF1N2A9FxEDAEo/ny+1twK7tus3EFjS/fIkSZIkqX50N2DNBiaW3k8EZrVrnxARW0TEYGAI8GDPSpQkSZKk+tDpM1gRcR0wGugXEa1AEzANuCEiPgc8A5wAkJnzI+IGYAGwEpicmasqVLskSZIk1ZROA1ZmnrSOXYeto/+FwIU9KUqSJEmS6lHRi1xIkiRJ0ibLgCVJkiRJBTFgSZIkSVJBDFiSJEmSVBADliRJkiQVxIAlSZIkSQUxYEmSJElSQQxYkiRJklQQA5YkSZIkFcSAJUmSJEkFMWBJkiRJUkE2r3YBktRec3NztUuQJEnqNmewJEmSJKkgPZrBiojFwKvAKmBlZjZGxA7ATGAQsBg4MTP/3LMyJUmSJKn2FTGD9bHMHJ6ZjaXtKcCdmTkEuLO0LUmSJEkbvUrcIjgOmFF6PwM4tgLXkCRJkqSa09OAlcBtETEnIiaV2nbKzKUApZ87dnRgREyKiJaIaFm2bFkPy5AkSZKk6uvpKoKjMnNJROwI3B4RT5R7YGZeDlwO0NjYmD2sQ5IkSZKqrkczWJm5pPTzeeBGYD/guYgYAFD6+XxPi5QkSZKketDtgBURW0dEn9XvgTHAPGA2MLHUbSIwq6dFSpIkSVI96MktgjsBN0bE6vNcm5m/ioiHgBsi4nPAM8AJPS9TkiRJkmpftwNWZv4B+EgH7cuBw3pSlCRJkiTVo54uciFJkiSgubm52iUUoqmpqdolSHXNgCVtBDaW/6hLkiTVu0r8oWFJkiRJ2iQZsCRJkiSpIAYsSZIkSSqIAUuSJEmSCmLAkiRJkqSCGLAkSZIkqSAGLEmSJEkqiAFLkiRJkgpiwJIkSZKkgmxe7QKkamlubq52CZIkSdrIVGwGKyLGRsSTEbEoIqZU6jqSJEmSVCsqMoMVEb2AS4DDgVbgoYiYnZkLKnE9SZIkFWNjusOjqamp2iVoE1SpGaz9gEWZ+YfM/AtwPTCuQteSJEmSpJpQqWewdgGebbfdCuzfvkNETAImlTZXRMSTHZynH/BCRSpUpTl29cuxq0+OW/1y7OqT41YHpk6d2lGzY1efanHcduuosVIBKzpoy3dtZF4OXL7ek0S0ZGZjkYVpw3Ds6pdjV58ct/rl2NUnx61+OXb1qZ7GrVK3CLYCu7bbHggsqdC1JEmSJKkmVCpgPQQMiYjBEfE+YAIwu0LXkiRJkqSaUJFbBDNzZUScCdwK9AKuzMz53TjVem8hVE1z7OqXY1efHLf65djVJ8etfjl29aluxi0ys/NekiRJkqROVewPDUuSJEnSpsaAJUmSJEkFMWBJkiRJUkEMWJIkSZJUEAOWJEmSJBXEgCVJkiRJBTFgSZIkSVJBDFiSJEmSVBADliRJkiQVxIAlSZIkSQUxYEmSJElSQQxYkiRJklQQA5YkSZIkFcSAJUmqmIi4NSLO66B9XET8KSI2r9B1z4+IxyJiZURMXU+/qyIiI+Jv19NncUS8ERErSq/b2u0bHRHvtNu3IiImFvxxJEl1xIAlSaqk6cCpERFrtZ8KXJOZK8s5STeC2CLgn4Cb13POg4APlnm+ozNzm9JrzFr7lrTbt01mzuhirZKkjYgBS5JUSTcBOwAfXd0QEdsDRwGzI+L+iHgpIpZGxMUR8b52/TIiJkfEQmBhVy6amTMy8xbg1Y72lwLbvwJndvkTSZK0HgYsSVLFZOYbwA3AZ9o1nwg8AawAzgL6AQcChwFfWOsUxwL7A0MBIuLRUiDr6HVpF0o7C7gnMx8ts/81EbEsIm6LiI+stW/HiHguIp6OiO9HxNZdqEOStJExYEmSKm0GcEJEbFna/gwwIzPnZOYDmbkyMxcD/wYcstax38rMF0tBjczcOzPfv47X2uGsQxGxK3AG8I0y6z8FGATsBvwauDUi3l/a9wQwHBgAHAqMAC4q87ySpI2QAUuSVFGZeS+wDBgXEX8DjASujYjdI+I/S4tdvAJ8k7bZrPaerUBJPwDOy8yXy+mcmfdl5huZ+Xpmfgt4idItj5n5p8xckJnvZObTtD339akK1CxJqhMGLEnShnA1bTNXpwK3ZeZzwI9omwEakpnbAl8H1l4MI9tvRMT8tVbsa/+6rMxaDgO+Uwp2fyq13R8RJ5d5fHZQZzn7JEmbgIosjytJ0lquBv4Z2Ju2558A+gCvACsi4sPA52mb6VqnzNyznItFRG+gF22/SNw8IhqAtzNzFbA77/4F41LgaOB3HZznA8CuwEOlY75I2yzbfaX9o4E/0DbTNhCYBswqp0ZJ0sbJGSxJUsWVnrH6b2BrYHap+avAybSt9HcFMLPAS14BvAGcBJxben9qqZbnS7f2/SkzV89gvbD6Oa+IuKzdbFgf2mba/gz8DzAW+HhmLi/t3xe4H3it9PnmAV8q8HNIkupMZGbnvSRJkiRJnXIGS5IkSZIKYsCSJEmSpIIYsCRJkiSpIAYsSZIkSSpITSzT3q9fvxw0aFC1y5AkSZKkssyZM+eFzOy/dntNBKxBgwbR0tJS7TIkSZIkqSwR8ceO2r1FUJIkSZIKYsCSJEmSpIIYsCRJkiSpIDXxDJYkadPT3Nxc7RIK1dTUVO0SJNWgt99+m9bWVt58881ql6JuamhoYODAgfTu3bus/gYsSZIkqUJaW1vp06cPgwYNIiKqXY66KDNZvnw5ra2tDB48uKxjvEVQkiRJqpA333yTvn37Gq7qVETQt2/fLs1AGrAkSZKkCjJc1beujp8BS5IkSZIK4jNYkiRJ0gZS9AI/5Syw09rayuTJk1mwYAHvvPMORx11FN/5znd43/vet85jXnrpJa699lq+8IUvALBkyRK+9KUv8dOf/rTHNU+dOpVtttmGr371q+9pv+KKK+jfvz+vvfYaw4YN44ILLmDo0KFdvsbs2bNZsGABU6ZM6XG9XeUMliRJkrSRykyOO+44jj32WBYuXMjvf/97VqxYwbnnnrve41566SUuvfTSNds777xzIeGqM2eddRZz585l4cKFjB8/nkMPPZRly5Z1+TzHHHNMVcIVGLAkSZKkjdZdd91FQ0MDn/3sZwHo1asX3//+97nyyit5/fXXmT59OuPGjWPs2LF86EMfWjPDNmXKFJ566imGDx/O1772NRYvXsxee+0FwPTp0zn22GM5+uijGTx4MBdffDEXXXQR++yzDwcccAAvvvgiAFdccQUjR47kIx/5CMcffzyvv/56l2ofP348Y8aM4dprrwVgzpw5HHLIIYwYMYIjjjiCpUuXAvDDH/6QoUOHsvfeezNhwoQ1NZ555pkAPPXUUxxwwAGMHDmSb3zjG2yzzTYA3H333YwePZpPfepTfPjDH+aUU04hM3vyzw14i6AkSYXYmP6ul3/TS9p4zJ8/nxEjRryrbdttt+UDH/gAixYtAuDBBx9k3rx5bLXVVowcOZIjjzySadOmMW/ePObOnQvA4sWL33WOefPm8cgjj/Dmm2/yt3/7t/zLv/wLjzzyCGeddRZXX301X/nKVzjuuOP4+7//ewD++Z//mR//+Md88Ytf7FL9++67L0888QRvv/02X/ziF5k1axb9+/dn5syZnHvuuVx55ZVMmzaNp59+mi222IKXXnrpPef48pe/zJe//GVOOukkLrvssnfte+SRR5g/fz4777wzo0aN4r777uOggw7qUo1rcwZLkiRJ2khlZoer4LVvP/zww+nbty9bbrklxx13HPfee2+n5/3Yxz5Gnz596N+/P9tttx1HH300AMOGDVsTxubNm8dHP/pRhg0bxjXXXMP8+fO7VT/Ak08+ybx58zj88MMZPnw4F1xwAa2trQDsvffenHLKKfzkJz9h883fO390//33c8IJJwBw8sknv2vffvvtx8CBA9lss80YPnz4e4JkdxiwJEmSpI3UnnvuSUtLy7vaXnnlFZ599lk++MEPAu9dhrycZcm32GKLNe8322yzNdubbbYZK1euBOC0007j4osv5rHHHqOpqalLf0tqtUceeYQ99tiDzGTPPfdk7ty5zJ07l8cee4zbbrsNgJtvvpnJkyczZ84cRowYseb65Wj/OXr16tWlY9fFgCVJkiRtpA477DBef/11rr76agBWrVrFP/7jP3Laaaex1VZbAXD77bfz4osv8sYbb3DTTTcxatQo+vTpw6uvvtqja7/66qsMGDCAt99+m2uuuabLx//sZz/jtttu46STTuJDH/oQy5Yt4/777wfg7bffZv78+bzzzjs8++yzfOxjH+Pb3/42L730EitWrHjXeQ444AB+9rOfAXD99df36DOVo9NnsCJiV+Bq4H8B7wCXZ+b/i4gdgJnAIGAxcGJm/rl0zDnA54BVwJcy89aKVC9JkiTVkQ39jGNEcOONN/KFL3yB888/n3feeYdPfOITfPOb31zT56CDDuLUU09l0aJFnHzyyTQ2NgIwatQo9tprLz7+8Y8zefLkLl/7/PPPZ//992e33XZj2LBhZQW273//+/zkJz/htddeY6+99uKuu+6if//+APz0pz/lS1/6Ei+//DIrV67kK1/5Crvvvjuf/vSnefnll8lMzjrrLN7//ve/65w/+MEP+PSnP833vvc9jjzySLbbbrsuf5auiM5WyoiIAcCAzHw4IvoAc4BjgdOAFzNzWkRMAbbPzLMjYihwHbAfsDNwB7B7Zq5a1zUaGxtz7alLSdLGbWNaFGJj4yIXUnEef/xx9thjj2qXsU7Tp0+npaWFiy++uNqlVMzrr7/OlltuSURw/fXXc9111zFr1qwunaOjcYyIOZnZuHbfTmewMnMpsLT0/tWIeBzYBRgHjC51mwHcDZxdar8+M98Cno6IRbSFrfu79CkkSZIkqYfmzJnDmWeeSWby/ve/nyuvvLKi1+vSMu0RMQjYB/gtsFMpfJGZSyNix1K3XYAH2h3WWmqTJEmSVENOO+00TjvttGqXUVEf/ehH+d3vfrfBrlf2IhcRsQ3wM+ArmfnK+rp20Pae+xAjYlJEtERES3f+OrMkSZJUD4r447Wqnq6OX1kBKyJ60xaursnMn5eanys9n7X6Oa3nS+2twK7tDh8ILOmg0MszszEzG1c/uCZJkiRtTBoaGli+fLkhq05lJsuXL6ehoaHsY8pZRTCAHwOPZ+ZF7XbNBiYC00o/Z7VrvzYiLqJtkYshwINlVyRJkiRtJAYOHEhrayvesVW/GhoaGDhwYNn9y3kGaxRwKvBYRMwttX2dtmB1Q0R8DngGOAEgM+dHxA3AAmAlMHl9KwhKkiRJG6vevXszePDgapehDaicVQTvpePnqgAOW8cxFwIX9qAuSZIkSao7ZS9yIUmSJElaPwOWJEmSJBXEgCVJkiRJBTFgSZIkSVJBDFiSJEmSVJBylmmXJNWA5ubmapcgSZI64QyWJEmSJBXEgCVJkiRJBTFgSZIkSVJBDFiSJEmSVBADliRJkiQVxIAlSZIkSQXpNGBFxJUR8XxEzGvXNjUi/ici5pZen2i375yIWBQRT0bEEZUqXJIkSZJqTTkzWNOBsR20fz8zh5devwSIiKHABGDP0jGXRkSvooqVJEmSpFrWacDKzHuAF8s83zjg+sx8KzOfBhYB+/WgPkmSJEmqGz15BuvMiHi0dAvh9qW2XYBn2/VpLbW9R0RMioiWiGhZtmxZD8qQJEmSpNrQ3YD1I+CDwHBgKfC9Unt00Dc7OkFmXp6ZjZnZ2L9//26WIUmSJEm1o1sBKzOfy8xVmfkOcAV/vQ2wFdi1XdeBwJKelShJkiRJ9aFbASsiBrTb/CSweoXB2cCEiNgiIgYDQ4AHe1aiJEmSJNWHzTvrEBHXAaOBfhHRCjQBoyNiOG23/y0GzgDIzPkRcQOwAFgJTM7MVRWpXJIkSZJqTGR2+IjUBtXY2JgtLS3VLkOSalpzc3O1S5DqUlNTU7VLkLQRiog5mdm4dntPVhGUJEmSJLVjwJIkSZKkghiwJEmSJKkgBixJkiRJKogBS5IkSZIKYsCSJEmSpIIYsCRJkiSpIAYsSZIkSSqIAUuSJEmSCmLAkiRJkqSCGLAkSZIkqSAGLEmSJEkqSKcBKyKujIjnI2Jeu7YdIuL2iFhY+rl9u33nRMSiiHgyIo6oVOGSJEmSVGvKmcGaDoxdq20KcGdmDgHuLG0TEUOBCcCepWMujYhehVUrSZIkSTWs04CVmfcAL67VPA6YUXo/Azi2Xfv1mflWZj4NLAL2K6ZUSZIkSapt3X0Ga6fMXApQ+rljqX0X4Nl2/VpLbe8REZMioiUiWpYtW9bNMiRJkiSpdhS9yEV00JYddczMyzOzMTMb+/fvX3AZkiRJkrThdTdgPRcRAwBKP58vtbcCu7brNxBY0v3yJEmSJKl+dDdgzQYmlt5PBGa1a58QEVtExGBgCPBgz0qUJEmSpPqweWcdIuI6YDTQLyJagSZgGnBDRHwOeAY4ASAz50fEDcACYCUwOTNXVah2SZIkSaopnQaszDxpHbsOW0f/C4ELe1KUJBWlubm52iVIkqRNSNGLXEiSJEnSJsuAJUmSJEkFMWBJkiRJUkEMWJIkSZJUEAOWJEmSJBXEgCVJkiRJBel0mXZJkqR6tjH9uYampqZqlyCpE85gSZIkSVJBDFiSJEmSVBADliRJkiQVxIAlSZIkSQUxYEmSJElSQXq0imBELAZeBVYBKzOzMSJ2AGYCg4DFwImZ+eeelSlJkiRJta+IGayPZebwzGwsbU8B7szMIcCdpW1JkiRJ2uhV4hbBccCM0vsZwLEVuIYkSZIk1ZyeBqwEbouIORExqdS2U2YuBSj93LGjAyNiUkS0RETLsmXLeliGJEmSJFVfj57BAkZl5pKI2BG4PSKeKPfAzLwcuBygsbExe1iHJEmSJFVdj2awMnNJ6efzwI3AfsBzETEAoPTz+Z4WKUmSJEn1oNsBKyK2jog+q98DY4B5wGxgYqnbRGBWT4uUJEmSpHrQk1sEdwJujIjV57k2M38VEQ8BN0TE54BngBN6XqYkSZIk1b5uB6zM/APwkQ7alwOH9aQoSdXT3Nxc7RIkSZLqViWWaZckSZKkTZIBS5IkSZIKYsCSJEmSpIIYsCRJkiSpIAYsSZIkSSqIAUuSJEmSCmLAkiRJkqSCGLAkSZIkqSDd/kPDkiRJ2rA2tj8G39TUVO0SpMI5gyVJkiRJBTFgSZIkSVJBvEVQKsDGdsuGJEmSuqdiM1gRMTYinoyIRRExpVLXkSRJkqRaUZEZrIjoBVwCHA60Ag9FxOzMXFCJ60mSJKn+bGx3gLhoh6ByM1j7AYsy8w+Z+RfgemBcha4lSZIkSTWhUs9g7QI82267Fdi/fYeImARMKm2uiIgnOzhPP+CFilSoSnPs6pdjV58ct/rl2NUnx61+VWzspk6dWonTqk0tfud266ixUgErOmjLd21kXg5cvt6TRLRkZmORhWnDcOzql2NXnxy3+uXY1SfHrX45dvWpnsatUrcItgK7ttseCCyp0LUkSZIkqSZUKmA9BAyJiMER8T5gAjC7QteSJEmSpJpQkVsEM3NlRJwJ3Ar0Aq7MzPndONV6byFUTXPs6pdjV58ct/rl2NUnx61+OXb1qW7GLTKz816SJEmSpE5V7A8NS5IkSdKmxoAlSZIkSQWpyYAVEd+JiCci4tGIuDEi3l9qHxQRb0TE3NLrsiqXqrWsa+xK+86JiEUR8WREHFHFMrWWiDghIuZHxDsR0diu3e9cjVvX2JX2+Z2rAxExNSL+p9337BPVrknrFxFjS9+rRRExpdr1qDwRsTgiHit9z1qqXY/WLSKujIjnI2Jeu7YdIuL2iFhY+rl9NWtcn5oMWMDtwF6ZuTfwe+Ccdvueyszhpdf/qU55Wo8Oxy4ihtK2muSewFjg0ojoVbUqtbZ5wHHAPR3s8ztX2zocO79zdef77b5nv6x2MVq30vfoEuDjwFDgpNL3TfXhY6XvWV38PaVN2HTa/tvV3hTgzswcAtxZ2q5JNRmwMvO2zFxZ2nyAtr+jpTqwnrEbB1yfmW9l5tPAImC/atSo98rMxzPzyWrXoa5bz9j5nZMqYz9gUWb+ITP/AlxP2/dNUkEy8x7gxbWaxwEzSu9nAMduyJq6oiYD1lr+P+CWdtuDI+KRiPhNRHy0WkWpLO3Hbhfg2Xb7Wkttqn1+5+qT37n6cmbp1uora/m2FwF+t+pZArdFxJyImFTtYtRlO2XmUoDSzx2rXM86VeTvYJUjIu4A/lcHu87NzFmlPucCK4FrSvuWAh/IzOURMQK4KSL2zMxXNkjRAro9dtFBf/9GwAZUzrh1wO9cDejm2PmdqyHrG0PgR8D5tI3P+cD3aPsFlWqT3636NSozl0TEjsDtEfFEaaZEKlTVAlZm/u/17Y+IicBRwGFZ+mNdmfkW8Fbp/ZyIeArYHfBBxQ2oO2NH22/4dm3XbSCwpDIVqiOdjds6jvE7VwO6M3b4nasp5Y5hRFwB/GeFy1HP+N2qU5m5pPTz+Yi4kbbbPQ1Y9eO5iBiQmUsjYgDwfLULWpeavEUwIsYCZwPHZObr7dr7r35IOyL+BhgC/KE6Vaoj6xo7YDYwISK2iIjBtI3dg9WoUeXzO1fX/M7VidL/KKz2SdoWLlHteggYEhGDI+J9tC0mM7vKNakTEbF1RPRZ/R4Yg9+1ejMbmFh6PxFY1x0cVVe1GaxOXAxsQdv0LcADpdXLDgbOi4iVwCrg/2Tm2g/Aqbo6HLvMnB8RNwALaLt1cHJmrqpinWonIj4J/CvQH7g5IuZm5hH4nat56xo7v3N15dsRMZy228wWA2dUtRqtV2aujIgzgVuBXsCVmTm/ymWpczsBN5b+32Rz4NrM/FV1S9K6RMR1wGigX0S0Ak3ANOCGiPgc8AxwQvUqXL/46x1ckiRJkqSeqMlbBCVJkiSpHhmwJEmSJKkgBixJkiRJKogBS5IkSZIKYsCSJEmSpIIYsCRJkiSpIAYsSZIkSSrI/w9lBtkuovFXrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3, 1, sharex=True, figsize=(12, 6))\n",
    "ax1, ax2, ax3 = axs.ravel()\n",
    "\n",
    "taus_every_1_ipw = list(map(tau_ipw, exps_every_1))\n",
    "ax1.hist(taus_every_1_ipw, label=\"Every Period\", color=\"0.5\")\n",
    "ax1.set_title(f\"Var={np.round(np.var(taus_every_1_ipw), 2)}\")\n",
    "ax1.legend()\n",
    "\n",
    "\n",
    "taus_every_3_ipw = list(map(tau_ipw, exps_every_3))\n",
    "ax2.hist(taus_every_3_ipw, label=\"Every 3 Periods\", color=\"0.5\");\n",
    "ax2.set_title(f\"Var={np.round(np.var(taus_every_3_ipw), 2)}\")\n",
    "ax2.legend()\n",
    "\n",
    "taus_opt_ipw = list(map(tau_ipw, exps_opt))\n",
    "ax3.hist(taus_opt_ipw, label=\"Optimal Design\", color=\"0.5\");\n",
    "ax3.set_title(f\"Var={np.round(np.var(taus_opt_ipw), 2)}\")\n",
    "ax3.legend()\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c8ad207",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-05T11:56:13.934223Z",
     "start_time": "2023-02-05T11:56:13.931098Z"
    }
   },
   "source": [
    "### Robust Variance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b9964280",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:50.901893Z",
     "start_time": "2023-05-17T10:32:50.895125Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rand_points</th>\n",
       "      <th>d</th>\n",
       "      <th>delivery_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>5.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>5.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>8.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>8.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>10.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>7.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rand_points  d  delivery_time\n",
       "0         True  0           5.84\n",
       "1        False  0           5.40\n",
       "2        False  0           8.86\n",
       "3        False  0           8.79\n",
       "4         True  0          10.93\n",
       "5        False  0           7.02"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_opt = pd.read_csv(\"./data/sb_exp_opt.csv\")\n",
    "df_opt.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4d256e26",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:50.907700Z",
     "start_time": "2023-05-17T10:32:50.903196Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-9.921016949152545"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tau_hat = ipw_switchback(df_opt[\"d\"],\n",
    "                         df_opt[\"delivery_time\"],\n",
    "                         df_opt[\"rand_points\"],\n",
    "                         m=2, p=0.5)\n",
    "\n",
    "tau_hat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "68a6edc4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:50.911916Z",
     "start_time": "2023-05-17T10:32:50.908998Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1],\n",
       "       [1, 2],\n",
       "       [2, 3]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.vstack(np.hsplit(np.array([1,1,1,2,2,3]), 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fc086811",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:50.916370Z",
     "start_time": "2023-05-17T10:32:50.913175Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diff(np.vstack(np.hsplit(np.array([1,1,0,0,0,0]), 3))[:, 0]) == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e3e2d950",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:50.921232Z",
     "start_time": "2023-05-17T10:32:50.917674Z"
    }
   },
   "outputs": [],
   "source": [
    "def var_opt_design(d_opt, y_opt, T, m):\n",
    "    \n",
    "    assert ((T//m == T/m)\n",
    "            & (T//m >= 4)), \"T must be divisible by m and T/m >= 4\"\n",
    "    \n",
    "    # discard 1st block\n",
    "    y_m_blocks = np.vstack(np.hsplit(y_opt, int(T/m))).sum(axis=1)[1:]\n",
    "    \n",
    "    # take 1st column\n",
    "    d_m_blocks = np.vstack(np.split(d_opt, int(T/m))[1:])[:, 0] \n",
    "    \n",
    "    return (\n",
    "        8*y_m_blocks[0]**2 \n",
    "        + (32*y_m_blocks[1:-1]**2*(np.diff(d_m_blocks)==0)[:-1]).sum()\n",
    "        + 8*y_m_blocks[-1]**2 \n",
    "    ) / (T-m)**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1acee734",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-17T10:32:50.931116Z",
     "start_time": "2023-05-17T10:32:50.922346Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-18.490627362048095, -1.351406536256997]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "se_hat = np.sqrt(var_opt_design(df_opt[\"d\"],\n",
    "                                df_opt[\"delivery_time\"],\n",
    "                                T=120, m=2))\n",
    "\n",
    "[tau_hat - 1.96*se_hat, tau_hat + 1.96*se_hat]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a6afcdb",
   "metadata": {},
   "source": [
    "## Key Ideas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ec71b0d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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